Publications
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Pattiwar, Shravan Kumar; Tilleman, Myrthe; Kousias, Konstantinos; Saxena, Paresh; Alay, Ozgu & Griwodz, Carsten
(2026).
Impact of Latency on User Experience in Immersive Teleoperation: A Study with Motion Capture Gloves and Dexterous Robotic Hand.
In Malekian, Reza & Singh, Dhananjay (Ed.),
2025 IEEE Smart World Congress (SWC).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331575984.
p. 1787–1794.
doi:
10.1109/swc65939.2025.00277.
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Understanding latency tolerance in teleoperated systems is crucial to ensure smooth human-robot interaction (HRI). In this study, we investigate the effects of network-induced delays on teleoperation using a controlled setup featuring a Rokoko Smartglove and a Shadow Robot Dexterous Hand. Through our experiments, users control the robotic hand in real-time while receiving visual feedback either directly or via a VR headset, delivering an immersive, metaverse-oriented experience. To introduce controlled delays, we utilize an Ingress Delay Mechanism for precise latency application. A user study involving 33 participants was organized to evaluate the impact of latency on user experience in terms of responsiveness, naturalness, control, and level of frustration. Results show that VR-based teleoperation is more sensitive to delays than direct HRI. In particular, perceived responsiveness decreased significantly with an additional 200 ms of delay, while frustration levels showed a significant increase with 150 ms of additional delay. These findings provide valuable insights towards optimizing future immersive teleoperation systems.
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Kousias, Konstantinos; Rajiullah, Mohammad; Caso, Giuseppe; Alay, Ozgu; Brunstrom, Anna & Ali, Usman
[Show all 9 contributors for this article]
(2024).
Empirical performance analysis and ML-based modeling of 5G non-standalone networks.
Computer Networks.
ISSN 1389-1286.
241.
doi:
10.1016/j.comnet.2024.110207.
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Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.
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Kjenstad, Emil Magnar; Ringsby, Halvor Kristian; Gaeb, Rahel Jamal; Erdem, Cagri; Kousias, Konstantinos & Alay, Ozgu
[Show all 7 contributors for this article]
(2023).
Experiencing Rotation and Curvature Gain for Redirected Walking in Virtual Reality,
MMVE '23: Proceedings of the 15th International Workshop on Immersive Mixed and Virtual Environment Systems.
Association for Computing Machinery (ACM).
ISSN 9798400701894.
p. 1–6.
doi:
10.1145/3592834.3592874.
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The proliferation of virtual reality (VR) interaction in the wake of the Metaverse trend will place an increasing number of applications and services into virtual environments (VEs). Over the recent years, interactions with the VE have been studied intensely, but very frequently, such interactions are focused on stationary users or users who leverage specialized contraptions to act in the VE (e.g., omni-directional treadmills). The free movement in the VE tends to be achieved by controller input, which creates a huge hurdle to enter and act in it in a natural manner. The target of this study is the translation of the natural walking motion from the real environment (RE) into the VE. In particular, we aim to explore to which extent redirected walking (RW) is achievable without being noticed by the users. Towards this goal, we test two RW methods, i.e., rotation gain and curvature gain. According to the responses of the participants in our study, we find that there is a statistically significant difference with 90% confidence between the levels of gains for rotation gain. On the other hand, levels of gains for curvature gain are not noticeable (i.e., no statistically significant difference is observed).
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Abbas, Muhammad Tahir; Grinnemo, Karl-Johan; Eklund, Johan; Alfredsson, Stefan; Rajiullah, Mohammad & Brunstrom, Anna
[Show all 9 contributors for this article]
(2022).
Energy-Saving Solutions for Cellular Internet of Things-A Survey.
IEEE Access.
10,
p. 62073–62096.
doi:
10.1109/ACCESS.2022.3182400.
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The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.
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Ali, Usman; Caso, Giuseppe; Nardis, Luca De; Kousias, Konstantinos; Rajiullah, Mohammad & Alay, Ozgu
[Show all 9 contributors for this article]
(2022).
Large-Scale Dataset for the Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks.
Data.
7(3).
doi:
10.3390/data7030034.
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Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3–3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde & Schwarz (R&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models.
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Ali, Usman; Caso, Giuseppe; Nardis, Luca De; Kousias, Konstantinos; Rajiullah, Mohammad & Alay, Ozgu
[Show all 9 contributors for this article]
(2022).
Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks.
Future Internet.
14(8).
doi:
10.3390/fi14080239.
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The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.
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Kousias, Konstantinos; Pappas, Apostolos; Alay, Özgü & Riegler, Michael
(2020).
Long short term memory networks for bandwidth forecasting in mobile broadband networks under mobility.
arXiv preprint arXiv:2011.10563.
Full text in Research Archive
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Kousias, Konstantinos; Ali, Shaukat & Yue, Tao
(2022).
WTT4Oslo: Improving Quality of IoT-based Welfare Technology Solutions in the City of Oslo.
Full text in Research Archive
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Kousias, Konstantinos; Alay, Özgü; Argyriou, Antonios & Riegler, Michael
(2019).
Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks.
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In recent years, Downlink (DL) throughput estima- tion in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged.In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over- the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled test- ing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experi- ment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15% can be achieved for a MdAPE of 20%.
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Kousias, Konstantinos; Riegler, Michael; Alay, Özgü & Argyriou, Antonios
(2018).
HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization.
Full text in Research Archive
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Kousias, Konstantinos; Midoglu, Cise; Alay, Özgü; Lutu, Andra & Riegler, Michael
(2017).
The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset.
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Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.
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Published
Feb. 21, 2023 10:06 AM
- Last modified
Feb. 22, 2023 4:01 PM
